The Grammar of Graphics

• Visualization concept created by Leland Wilkinson (The Grammar of Graphics, 1999)

• attempt to taxonimize the basic elements of statistical graphics

• Adapted for R by Hadley Wickham (2009)

  • consistent and compact syntax to describe statistical graphics

  • highly modular as it breaks up graphs into semantic components

  • ggplot2 is not meant as a guide to which graph to use and how to best convey your data (more on that later), but it does have some strong opinions.

Terminology

A statistical graphic is a…

• mapping of data

• which may be statistically transformed (summarized, log-transformed, etc.)

• to aesthetic attributes (color, size, xy-position, etc.)

• using geometric objects (points, lines, bars, etc.)

• and mapped onto a specific facet and coordinate system

Anatomy of a ggplot call

ggplot(
  data = [dataframe], 
  mapping = aes(
    x = [var x], y = [var y], 
    color = [var color], 
    shape = [var shape],
    ...
  )
) +
  geom_[some geom](
    mapping = aes(
      color = [var geom color],
      ...
    )
  ) +
  ... # other geometries
  scale_[some axis]_[some scale]() +
  facet_[some facet]([formula]) +
  ... # other options

Data - Palmer Penguins

Measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

library(palmerpenguins)
penguins

# A tibble: 344 × 8
   species island    bill_le…¹ bill_…² flipp…³ body_…⁴ sex  
   <fct>   <fct>         <dbl>   <dbl>   <int>   <int> <fct>
 1 Adelie  Torgersen      39.1    18.7     181    3750 male 
 2 Adelie  Torgersen      39.5    17.4     186    3800 fema…
 3 Adelie  Torgersen      40.3    18       195    3250 fema…
 4 Adelie  Torgersen      NA      NA        NA      NA <NA> 
 5 Adelie  Torgersen      36.7    19.3     193    3450 fema…
 6 Adelie  Torgersen      39.3    20.6     190    3650 male 
 7 Adelie  Torgersen      38.9    17.8     181    3625 fema…
 8 Adelie  Torgersen      39.2    19.6     195    4675 male 
 9 Adelie  Torgersen      34.1    18.1     193    3475 <NA> 
10 Adelie  Torgersen      42      20.2     190    4250 <NA> 
# … with 334 more rows, 1 more variable: year <int>, and
#   abbreviated variable names ¹​bill_length_mm,
#   ²​bill_depth_mm, ³​flipper_length_mm, ⁴​body_mass_g

A basic ggplot

ggplot(
  data = penguins, 
  mapping = aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) +
  geom_point() +
  labs(
    title = "Bill depth and length",
    subtitle = paste(
      "Dimensions for Adelie, Chinstrap,",
      "and Gentoo Penguins"
    ),
    x = "Bill depth (mm)", 
    y = "Bill length (mm)",
    color = "Species"
  )

Argument names

Often we omit the names of first two arguments when building plots with ggplot().

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_depth_mm,
    y = bill_length_mm
  )
) +
  geom_point(
    mapping = aes(color = species)
  ) +
  scale_color_viridis_d()

ggplot(
  penguins,
  aes(
    x = bill_depth_mm,
    y = bill_length_mm
  )
) +
  geom_point(
    aes(color = species)
  ) +
  scale_color_viridis_d()

Aesthetics options

Commonly used characteristics of plotting geometries that can be mapped to a specific variable in the data, examples include:

• position (x, y)
• color
• shape
• size
• alpha (transparency)

Different geometries have different aesthetics that can be used - see the ggplot2 geoms help files for listings.

• Aesthetics given in ggplot() apply to all geoms.

• Aesthetics for a specific geom_*() can be overridden via mapping or as an argument.

color

ggplot(
  penguins,
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point(
    aes(color = species)
  )

Shape

Mapped to a different variable than color

ggplot(
  penguins,
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) +
  geom_point(
    aes(
      color = species,
      shape = island  
    )
  )

Shape

Mapped to same variable as color

ggplot(
  penguins,
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) +
  geom_point(
    aes(
      color = species,
      shape = species 
    )
  )

Size

Using a fixed value (note this value is outside of the aes call)

ggplot(
  penguins,
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) +
  geom_point(
    aes(
      color = species,
      shape = species
    ),
    size = 3
  )

Size

Mapped to a variable

ggplot(
  penguins,
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) +
  geom_point(
    aes(
      color = species,
      shape = species,
      size = body_mass_g
    ),
  )

Alpha

ggplot(
  penguins,
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) +
  geom_point(
    aes(
      color = species,
      shape = species,
      alpha = body_mass_g
    ),
    size = 3
  )

Mapping vs settings

• Mapping - Determine an aesthetic (the size, alpha, etc.) of a geom based on the values of a variable in the data
  • wrapped by aes() and pass as mapping argument to ggplot() or geom_*().

• Setting - Determine an aesthetic (the size, alpha, etc.) of a geom not based on the values of a variable in the data, usually a constant value.
  • passed directly into geom_*() as an argument.

Faceting

• Smaller plots that display different subsets of the data

• Useful for exploring conditional relationships and large data

• Sometimes referred to as “small multiples”

facet_grid

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point() +
  facet_grid(
    species ~ island
  )  

Compare with …

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point(
    aes(
      color = species,
      shape = island
    ), 
    size = 3
  )

facet_grid (cols)

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point() +
  facet_grid(
    ~ species
  )  

facet_grid (rows)

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point() +
  facet_grid(
    species ~ .
  )  

facet_wrap

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point() +
  facet_wrap(
    ~ species
  )

facet_wrap

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm
  )
) + 
  geom_point() +
  facet_wrap(
    ~ species, ncol = 2
  )

Faceting and color

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm, 
    color = species
  )
) +
  geom_point() +
  facet_grid(
    species ~ sex
  )

Hiding legend elements

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm, 
    color = species
  )
) +
  geom_point() +
  facet_grid(
    species ~ sex
  ) +
  guides(color = "none")

Histograms

ggplot(
  penguins, 
  aes(x = body_mass_g)
) +
  geom_histogram()

Histograms - bins

ggplot(
  penguins, 
  aes( x = body_mass_g )
) +
  geom_histogram(bins = 50)

Histograms - binwidth

ggplot(
  penguins, 
  aes( x = body_mass_g )
) +
  geom_histogram(binwidth = 250)

Histograms - color

ggplot(
  penguins, 
  aes(x = body_mass_g, 
      color = species )
) +
  geom_histogram(bins = 20)

Histograms - fill

ggplot(
  penguins, 
  aes(x = body_mass_g, 
      fill = species )
) +
  geom_histogram(bins = 20)

Histograms - position

ggplot(
  penguins, 
  aes(x = body_mass_g, 
      fill = species )
) + 
  geom_histogram(
    bins = 20, alpha = 0.5,
    position = "identity"
  )

Histograms - facets

ggplot(
  penguins, 
  aes(x = body_mass_g, 
      fill = species ) 
) +
  geom_histogram(bins = 20) +
  facet_grid(species ~ .) + 
  guides(fill = "none")

Density plot

ggplot(
  penguins, 
  aes(x = body_mass_g)
) +
  geom_density()

Density plot - fill

ggplot(
  penguins, 
  aes(x = body_mass_g, 
      fill = species )
) +
  geom_density(alpha = 0.25)

Density plot - adjust

ggplot(
  penguins,
  aes(x = body_mass_g,
     fill = species )
) +
  geom_density(
    adjust = 0.5, alpha = 0.25
  )

Violin plot

ggplot(penguins, 
  aes(x = species, fill = species,
      y = body_mass_g )
) +
  geom_violin()

Ridge plot

ggplot(
  penguins, 
  aes(x = body_mass_g,
      y = species, fill = species )
) +
  ggridges::geom_density_ridges(alpha = 0.5)

Ridge plot - more categories + dplyr

penguins %>%
  mutate(
    species_sex = paste0(
      species, " (", sex, ")"
    )
  ) %>%
  ggplot(
    aes(
      x = body_mass_g,
      y = species_sex,
      fill = species
    )
  ) +
  ggridges::geom_density_ridges(
    alpha = 0.5
  )

Box plot

ggplot(
  penguins, 
  aes(x = body_mass_g,
      y = species )
) +
  geom_boxplot()

Box plot - coord_flip

ggplot(
  penguins, 
  aes(x = body_mass_g,
      y = species )
) +
  geom_boxplot() + 
  coord_flip()

Box plot - swap coords

ggplot(
  penguins, 
  aes(x = species,
      y = body_mass_g )
) +
  geom_boxplot()

Scatter plot

ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species )
) +
  geom_point() 

Scatter plot - geom_smooth

ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species )
) +
  geom_point() +
  geom_smooth( fullrange = TRUE )

Scatter plot - geom_smooth w/ lm

ggplot(
  penguins, 
  aes(
    x = bill_depth_mm, 
    y = bill_length_mm, 
    color = species
  )
) +
  geom_point() +
  geom_smooth(
    method = "lm",
    se = FALSE,
    fullrange = TRUE
  )

Line plot

penguins %>%
  count(species, year) %>%
  ggplot(
    aes(x = year, y = n, 
        color = species, group = species )
  ) +
  geom_line()

Line plot - with points

penguins %>%
  count(species, year) %>%
  ggplot(
    aes(x = year, y = n, 
        color = species, group = species )
  ) +
  geom_line() + 
  geom_point()

Bar plot

ggplot(
  penguins, 
  aes( x = species )
) +
  geom_bar()

Stacked bar plot

ggplot(
  penguins, 
  aes( x = species,
       fill = island )
) +
  geom_bar()

Stacked relative frequency bar plot

ggplot(
  penguins, 
  aes( x = species,
       fill = island )
) +
  geom_bar(position = "fill")

Dodged bar plot

ggplot(
  penguins, 
  aes( x = species,
       fill = sex )
) +
  geom_bar(position = "dodge")

Exercise 1

Recreate, as faithfully as possible, the following plot using ggplot2 and the penguins data.

Exercise 2

Recreate, as faithfully as possible, the following plot from the palmerpenguin package readme in ggplot2.